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Content available remote Experimenting with manual and automated data mining pipelines on the FedCSIS 2024 Data Science Challenge
Buck Luisa, Furier Marc, Göktepe Okan Mert, Judák Jusztina, Lautenbach Max, Münker Gregor
Annals of Computer Science and Information Systems
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2024
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Vol. 39
751--754
EN
This paper reviews the 5th-best solution and results of the FedCSIS 2024 Data Science Challenge, which aimed to predict stock trends using financial indicators. It details the preprocessing, modelling, and tuning approaches and demonstrates, as well as the methods and techniques used to address the prediction problem effectively. Subsequently, the results of different experiments, including hyperparameter optimization on preprocessing steps and switching between different prediction targets, could be compared to manual experiments. Overall, a manually experienced model could be found to outperform hyperparameter-tuned pipelines.
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